GSE188217: Single-cell Transcriptomic Atlas of Gingival Mucosa in Type 2 Diabetes

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Info Studies were incorporated in a barcode, matrix, feature format. This format can be found on the 10x Genomics website for processing of single cell studies to obtain the gene expression matrices. For each study, the metadata incorporated in GEO were manually curated into profiles and the samples were separated based on applicable groups and conditions. The expression data from the cells for the samples within the same profile and condition were aggregated into an expression matrix with the cell barcodes having the sample name appended to it to ensure unique cell names.

Description (from GEO)

Submission Date: Nov 04, 2021

Summary: The oral gingival barrier is a constantly stimulated and dynamic environment where homeostasis is often disrupted, resulting in inflammatory periodontal diseases. Type 2 diabetes (T2D), a risk factor for periodontitis, has been reported to be associated with barrier dysfunction, but the effect and underlying mechanism are inconclusive. Herein, we performed single-cell RNA sequencing (scRNA-seq) of gingiva from leptin receptor-deficient (db/db) mice to understand the heterogeneity of gingival barrier in the context of T2D. Periodontal health of control mice is characterized by populations of Krt14+-expressing epithelial cells and Col1a1+-fibroblasts mediating immune homeostasis primarily through the enrichment of innate lymphoid cells. The db/db mice exhibit an impaired gingival barrier with spontaneous periodontal bone loss, and a decreased proportion of epithelial/stromal cells. We further observed stromal, particularly fibroblast immune hyperresponsiveness linked to recruitment of myeloid cell populations in gingiva from T2D mice. Analysis of ligand-receptor interaction pairs suggested inflammatory signaling between fibroblasts and myeloid cells, a main driver of diabetes-induced periodontal damage. Moreover, the "Immune-like" stromal cells contributed to gingival Th17/IL-17 hyperresponsiveness in T2D. Our work reveals transcriptional diversity of stromal cells and interaction with innate immune cells in T2D, and uncovers the "immune-like" fibroblast subsets participating in barrier homeostasis at the diabetic gingiva.

GEO Accession ID: GSE188217

PMID: No Pubmed ID

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Info Preprocessing and downstream analysis were computed using the scanpy Python library and the steps of processing followed the Seurat vignette. Cells and genes with no expression or very low expression were removed from the dataset based on a predefined threshold. The data was then normalized across the expression within the cells and log normalized. The top 2000 highly variable genes were extracted to be used for downstream analysis. For each of these aggregated data matrices, the clusters were computed using the leiden algorithm. Scanpy was then used to compute the PCA, t-SNE, and UMAPs. The points in the plots are labelled by their corresponding cell type labels. The cell type labels were computed using the wilcoxon method as the differential gene expression method. The top 250 genes were then used for enrichment analysis against the CellMarker library in order to determine the most appropriate cell type label with the lowest p-value.
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